Electronic marketing communications, such as e-mails and web pages, can be used by vendors and other businesses to induce customers and other users to access online content. For example, e-mails may be sent to users that have links to online video content, image content, or text content that describes different products or services. A user may click one or more links in an e-mail to access this online content via a website that is affiliated with a sender of the e-mail.
Some tools for developing marketing communications are used to identify which types of users should be targeted by specific campaigns. For example, users (or their visits to vendor websites) may have certain features indicating that the users are more likely to take a desirable action in response to receiving the marketing communication. Examples of relevant features include, but are not limited to, the user's demographic data, a search engine or other website used to access a vendor's content in response to a marketing communication from the vendor, a communication channel used to send a marketing communication to a user, etc. Examples of desirable actions include, but are not limited to, clicking on a link in a marketing communication that directs the user to a vendor's website, making a purchase at a vendor's website after accessing the website via the marketing communication, etc.
Tools for developing marketing communications use analytical models to determine relationships between certain features and desirable actions taken in response to marketing communication. For example, an analytical model receives, as inputs, data describing features of users (e.g., age group, location, education, etc.) and features of actions taken by users (e.g., clicks, conversions, etc.). The analytical model generates recommendations, probabilities, or other outputs indicating that users with one or more features will perform one or more favorable actions as a result of receiving a marketing communication.
These analytical models can be developed from historical data about different users. For example, a data set for thousands or millions of users can include data about one or more features (i.e., variables) of each user. The analytical model is “trained” to associate certain features with corresponding user actions in response to marketing communications. Through this training process, the analytical model learns which features should be used to select groups of users for future marketing communications.
Prior solutions for developing and training analytical models can present disadvantages. For example, developing a model that accounts for hundreds of variables may increase the processing requirements for the model, and (in many cases) may actually decrease the accuracy of the model. Furthermore, some of the features may not be particularly useful for predicting outcomes. For example, a user's age and demographic may have more influence on his or her actions than, for example, the type of web browser they use.
Therefore, it is desirable to reduce the number of features used in training an analytical model to improve efficiency and accuracy when training the model.